{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2 \n",
    "%matplotlib inline\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import keras.backend.tensorflow_backend as KTF\n",
    "import tensorflow as tf\n",
    "config = tf.ConfigProto()  \n",
    "config.gpu_options.allow_growth=True   \n",
    "session = tf.Session(config=config)\n",
    "KTF.set_session(session)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import keras \n",
    "from models.psenet import psenet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "shape = (None,None,3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Program Files\\Anaconda3\\lib\\site-packages\\keras_applications\\resnet50.py:265: UserWarning: The output shape of `ResNet50(include_top=False)` has been changed since Keras 2.2.0.\n",
      "  warnings.warn('The output shape of `ResNet50(include_top=False)` '\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(None, None)\n",
      "(None, None)\n",
      "(None, None)\n",
      "(None, None)\n",
      "(None, None)\n",
      "(None, None)\n",
      "(None, None)\n",
      "(None, None)\n",
      "(None, None)\n",
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "input_1 (InputLayer)            (None, None, None, 3 0                                            \n",
      "__________________________________________________________________________________________________\n",
      "lambda_1 (Lambda)               (None, None, None, 3 0           input_1[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "conv1_pad (ZeroPadding2D)       (None, None, None, 3 0           lambda_1[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv1 (Conv2D)                  (None, None, None, 6 9472        conv1_pad[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "bn_conv1 (BatchNormalization)   (None, None, None, 6 256         conv1[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "activation_1 (Activation)       (None, None, None, 6 0           bn_conv1[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "pool1_pad (ZeroPadding2D)       (None, None, None, 6 0           activation_1[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d_1 (MaxPooling2D)  (None, None, None, 6 0           pool1_pad[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "res2a_branch2a (Conv2D)         (None, None, None, 6 4160        max_pooling2d_1[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "bn2a_branch2a (BatchNormalizati (None, None, None, 6 256         res2a_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_2 (Activation)       (None, None, None, 6 0           bn2a_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res2a_branch2b (Conv2D)         (None, None, None, 6 36928       activation_2[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "bn2a_branch2b (BatchNormalizati (None, None, None, 6 256         res2a_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_3 (Activation)       (None, None, None, 6 0           bn2a_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res2a_branch2c (Conv2D)         (None, None, None, 2 16640       activation_3[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "res2a_branch1 (Conv2D)          (None, None, None, 2 16640       max_pooling2d_1[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "bn2a_branch2c (BatchNormalizati (None, None, None, 2 1024        res2a_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn2a_branch1 (BatchNormalizatio (None, None, None, 2 1024        res2a_branch1[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "add_1 (Add)                     (None, None, None, 2 0           bn2a_branch2c[0][0]              \n",
      "                                                                 bn2a_branch1[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "activation_4 (Activation)       (None, None, None, 2 0           add_1[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "res2b_branch2a (Conv2D)         (None, None, None, 6 16448       activation_4[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "bn2b_branch2a (BatchNormalizati (None, None, None, 6 256         res2b_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_5 (Activation)       (None, None, None, 6 0           bn2b_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res2b_branch2b (Conv2D)         (None, None, None, 6 36928       activation_5[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "bn2b_branch2b (BatchNormalizati (None, None, None, 6 256         res2b_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_6 (Activation)       (None, None, None, 6 0           bn2b_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res2b_branch2c (Conv2D)         (None, None, None, 2 16640       activation_6[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "bn2b_branch2c (BatchNormalizati (None, None, None, 2 1024        res2b_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_2 (Add)                     (None, None, None, 2 0           bn2b_branch2c[0][0]              \n",
      "                                                                 activation_4[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "activation_7 (Activation)       (None, None, None, 2 0           add_2[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "res2c_branch2a (Conv2D)         (None, None, None, 6 16448       activation_7[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "bn2c_branch2a (BatchNormalizati (None, None, None, 6 256         res2c_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_8 (Activation)       (None, None, None, 6 0           bn2c_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res2c_branch2b (Conv2D)         (None, None, None, 6 36928       activation_8[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "bn2c_branch2b (BatchNormalizati (None, None, None, 6 256         res2c_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_9 (Activation)       (None, None, None, 6 0           bn2c_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res2c_branch2c (Conv2D)         (None, None, None, 2 16640       activation_9[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "bn2c_branch2c (BatchNormalizati (None, None, None, 2 1024        res2c_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_3 (Add)                     (None, None, None, 2 0           bn2c_branch2c[0][0]              \n",
      "                                                                 activation_7[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "activation_10 (Activation)      (None, None, None, 2 0           add_3[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "res3a_branch2a (Conv2D)         (None, None, None, 1 32896       activation_10[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn3a_branch2a (BatchNormalizati (None, None, None, 1 512         res3a_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_11 (Activation)      (None, None, None, 1 0           bn3a_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res3a_branch2b (Conv2D)         (None, None, None, 1 147584      activation_11[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn3a_branch2b (BatchNormalizati (None, None, None, 1 512         res3a_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_12 (Activation)      (None, None, None, 1 0           bn3a_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res3a_branch2c (Conv2D)         (None, None, None, 5 66048       activation_12[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res3a_branch1 (Conv2D)          (None, None, None, 5 131584      activation_10[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn3a_branch2c (BatchNormalizati (None, None, None, 5 2048        res3a_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn3a_branch1 (BatchNormalizatio (None, None, None, 5 2048        res3a_branch1[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "add_4 (Add)                     (None, None, None, 5 0           bn3a_branch2c[0][0]              \n",
      "                                                                 bn3a_branch1[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "activation_13 (Activation)      (None, None, None, 5 0           add_4[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "res3b_branch2a (Conv2D)         (None, None, None, 1 65664       activation_13[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn3b_branch2a (BatchNormalizati (None, None, None, 1 512         res3b_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_14 (Activation)      (None, None, None, 1 0           bn3b_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res3b_branch2b (Conv2D)         (None, None, None, 1 147584      activation_14[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn3b_branch2b (BatchNormalizati (None, None, None, 1 512         res3b_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_15 (Activation)      (None, None, None, 1 0           bn3b_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res3b_branch2c (Conv2D)         (None, None, None, 5 66048       activation_15[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn3b_branch2c (BatchNormalizati (None, None, None, 5 2048        res3b_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_5 (Add)                     (None, None, None, 5 0           bn3b_branch2c[0][0]              \n",
      "                                                                 activation_13[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_16 (Activation)      (None, None, None, 5 0           add_5[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "res3c_branch2a (Conv2D)         (None, None, None, 1 65664       activation_16[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn3c_branch2a (BatchNormalizati (None, None, None, 1 512         res3c_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_17 (Activation)      (None, None, None, 1 0           bn3c_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res3c_branch2b (Conv2D)         (None, None, None, 1 147584      activation_17[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn3c_branch2b (BatchNormalizati (None, None, None, 1 512         res3c_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_18 (Activation)      (None, None, None, 1 0           bn3c_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res3c_branch2c (Conv2D)         (None, None, None, 5 66048       activation_18[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn3c_branch2c (BatchNormalizati (None, None, None, 5 2048        res3c_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_6 (Add)                     (None, None, None, 5 0           bn3c_branch2c[0][0]              \n",
      "                                                                 activation_16[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_19 (Activation)      (None, None, None, 5 0           add_6[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "res3d_branch2a (Conv2D)         (None, None, None, 1 65664       activation_19[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn3d_branch2a (BatchNormalizati (None, None, None, 1 512         res3d_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_20 (Activation)      (None, None, None, 1 0           bn3d_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res3d_branch2b (Conv2D)         (None, None, None, 1 147584      activation_20[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn3d_branch2b (BatchNormalizati (None, None, None, 1 512         res3d_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_21 (Activation)      (None, None, None, 1 0           bn3d_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res3d_branch2c (Conv2D)         (None, None, None, 5 66048       activation_21[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn3d_branch2c (BatchNormalizati (None, None, None, 5 2048        res3d_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_7 (Add)                     (None, None, None, 5 0           bn3d_branch2c[0][0]              \n",
      "                                                                 activation_19[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_22 (Activation)      (None, None, None, 5 0           add_7[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "res4a_branch2a (Conv2D)         (None, None, None, 2 131328      activation_22[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4a_branch2a (BatchNormalizati (None, None, None, 2 1024        res4a_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_23 (Activation)      (None, None, None, 2 0           bn4a_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4a_branch2b (Conv2D)         (None, None, None, 2 590080      activation_23[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4a_branch2b (BatchNormalizati (None, None, None, 2 1024        res4a_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_24 (Activation)      (None, None, None, 2 0           bn4a_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4a_branch2c (Conv2D)         (None, None, None, 1 263168      activation_24[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4a_branch1 (Conv2D)          (None, None, None, 1 525312      activation_22[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4a_branch2c (BatchNormalizati (None, None, None, 1 4096        res4a_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn4a_branch1 (BatchNormalizatio (None, None, None, 1 4096        res4a_branch1[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "add_8 (Add)                     (None, None, None, 1 0           bn4a_branch2c[0][0]              \n",
      "                                                                 bn4a_branch1[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "activation_25 (Activation)      (None, None, None, 1 0           add_8[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "res4b_branch2a (Conv2D)         (None, None, None, 2 262400      activation_25[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4b_branch2a (BatchNormalizati (None, None, None, 2 1024        res4b_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_26 (Activation)      (None, None, None, 2 0           bn4b_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4b_branch2b (Conv2D)         (None, None, None, 2 590080      activation_26[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4b_branch2b (BatchNormalizati (None, None, None, 2 1024        res4b_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_27 (Activation)      (None, None, None, 2 0           bn4b_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4b_branch2c (Conv2D)         (None, None, None, 1 263168      activation_27[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4b_branch2c (BatchNormalizati (None, None, None, 1 4096        res4b_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_9 (Add)                     (None, None, None, 1 0           bn4b_branch2c[0][0]              \n",
      "                                                                 activation_25[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_28 (Activation)      (None, None, None, 1 0           add_9[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "res4c_branch2a (Conv2D)         (None, None, None, 2 262400      activation_28[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4c_branch2a (BatchNormalizati (None, None, None, 2 1024        res4c_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_29 (Activation)      (None, None, None, 2 0           bn4c_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4c_branch2b (Conv2D)         (None, None, None, 2 590080      activation_29[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4c_branch2b (BatchNormalizati (None, None, None, 2 1024        res4c_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_30 (Activation)      (None, None, None, 2 0           bn4c_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4c_branch2c (Conv2D)         (None, None, None, 1 263168      activation_30[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4c_branch2c (BatchNormalizati (None, None, None, 1 4096        res4c_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_10 (Add)                    (None, None, None, 1 0           bn4c_branch2c[0][0]              \n",
      "                                                                 activation_28[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_31 (Activation)      (None, None, None, 1 0           add_10[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "res4d_branch2a (Conv2D)         (None, None, None, 2 262400      activation_31[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4d_branch2a (BatchNormalizati (None, None, None, 2 1024        res4d_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_32 (Activation)      (None, None, None, 2 0           bn4d_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4d_branch2b (Conv2D)         (None, None, None, 2 590080      activation_32[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4d_branch2b (BatchNormalizati (None, None, None, 2 1024        res4d_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_33 (Activation)      (None, None, None, 2 0           bn4d_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4d_branch2c (Conv2D)         (None, None, None, 1 263168      activation_33[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4d_branch2c (BatchNormalizati (None, None, None, 1 4096        res4d_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_11 (Add)                    (None, None, None, 1 0           bn4d_branch2c[0][0]              \n",
      "                                                                 activation_31[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_34 (Activation)      (None, None, None, 1 0           add_11[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "res4e_branch2a (Conv2D)         (None, None, None, 2 262400      activation_34[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4e_branch2a (BatchNormalizati (None, None, None, 2 1024        res4e_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_35 (Activation)      (None, None, None, 2 0           bn4e_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4e_branch2b (Conv2D)         (None, None, None, 2 590080      activation_35[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4e_branch2b (BatchNormalizati (None, None, None, 2 1024        res4e_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_36 (Activation)      (None, None, None, 2 0           bn4e_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4e_branch2c (Conv2D)         (None, None, None, 1 263168      activation_36[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4e_branch2c (BatchNormalizati (None, None, None, 1 4096        res4e_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_12 (Add)                    (None, None, None, 1 0           bn4e_branch2c[0][0]              \n",
      "                                                                 activation_34[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_37 (Activation)      (None, None, None, 1 0           add_12[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "res4f_branch2a (Conv2D)         (None, None, None, 2 262400      activation_37[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4f_branch2a (BatchNormalizati (None, None, None, 2 1024        res4f_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_38 (Activation)      (None, None, None, 2 0           bn4f_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4f_branch2b (Conv2D)         (None, None, None, 2 590080      activation_38[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4f_branch2b (BatchNormalizati (None, None, None, 2 1024        res4f_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_39 (Activation)      (None, None, None, 2 0           bn4f_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4f_branch2c (Conv2D)         (None, None, None, 1 263168      activation_39[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4f_branch2c (BatchNormalizati (None, None, None, 1 4096        res4f_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_13 (Add)                    (None, None, None, 1 0           bn4f_branch2c[0][0]              \n",
      "                                                                 activation_37[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_40 (Activation)      (None, None, None, 1 0           add_13[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "res5a_branch2a (Conv2D)         (None, None, None, 5 524800      activation_40[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn5a_branch2a (BatchNormalizati (None, None, None, 5 2048        res5a_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_41 (Activation)      (None, None, None, 5 0           bn5a_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res5a_branch2b (Conv2D)         (None, None, None, 5 2359808     activation_41[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn5a_branch2b (BatchNormalizati (None, None, None, 5 2048        res5a_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_42 (Activation)      (None, None, None, 5 0           bn5a_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res5a_branch2c (Conv2D)         (None, None, None, 2 1050624     activation_42[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res5a_branch1 (Conv2D)          (None, None, None, 2 2099200     activation_40[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn5a_branch2c (BatchNormalizati (None, None, None, 2 8192        res5a_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn5a_branch1 (BatchNormalizatio (None, None, None, 2 8192        res5a_branch1[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "add_14 (Add)                    (None, None, None, 2 0           bn5a_branch2c[0][0]              \n",
      "                                                                 bn5a_branch1[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "activation_43 (Activation)      (None, None, None, 2 0           add_14[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "res5b_branch2a (Conv2D)         (None, None, None, 5 1049088     activation_43[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn5b_branch2a (BatchNormalizati (None, None, None, 5 2048        res5b_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_44 (Activation)      (None, None, None, 5 0           bn5b_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res5b_branch2b (Conv2D)         (None, None, None, 5 2359808     activation_44[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn5b_branch2b (BatchNormalizati (None, None, None, 5 2048        res5b_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_45 (Activation)      (None, None, None, 5 0           bn5b_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res5b_branch2c (Conv2D)         (None, None, None, 2 1050624     activation_45[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn5b_branch2c (BatchNormalizati (None, None, None, 2 8192        res5b_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_15 (Add)                    (None, None, None, 2 0           bn5b_branch2c[0][0]              \n",
      "                                                                 activation_43[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_46 (Activation)      (None, None, None, 2 0           add_15[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "res5c_branch2a (Conv2D)         (None, None, None, 5 1049088     activation_46[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn5c_branch2a (BatchNormalizati (None, None, None, 5 2048        res5c_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_47 (Activation)      (None, None, None, 5 0           bn5c_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res5c_branch2b (Conv2D)         (None, None, None, 5 2359808     activation_47[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn5c_branch2b (BatchNormalizati (None, None, None, 5 2048        res5c_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_48 (Activation)      (None, None, None, 5 0           bn5c_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res5c_branch2c (Conv2D)         (None, None, None, 2 1050624     activation_48[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn5c_branch2c (BatchNormalizati (None, None, None, 2 8192        res5c_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_16 (Add)                    (None, None, None, 2 0           bn5c_branch2c[0][0]              \n",
      "                                                                 activation_46[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_49 (Activation)      (None, None, None, 2 0           add_16[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_1 (Conv2D)               (None, None, None, 2 524544      activation_49[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "resize_image_1 (resize_image)   (None, None, None, 2 0           conv2d_1[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_2 (Conv2D)               (None, None, None, 2 262400      activation_37[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_1 (Concatenate)     (None, None, None, 5 0           resize_image_1[0][0]             \n",
      "                                                                 conv2d_2[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_4 (Conv2D)               (None, None, None, 1 589952      concatenate_1[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_1 (BatchNor (None, None, None, 1 512         conv2d_4[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "activation_50 (Activation)      (None, None, None, 1 0           batch_normalization_1[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "resize_image_2 (resize_image)   (None, None, None, 1 0           activation_50[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_3 (Conv2D)               (None, None, None, 2 131328      activation_19[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_2 (Concatenate)     (None, None, None, 3 0           resize_image_2[0][0]             \n",
      "                                                                 conv2d_3[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_5 (Conv2D)               (None, None, None, 1 442496      concatenate_2[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_2 (BatchNor (None, None, None, 1 512         conv2d_5[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "activation_51 (Activation)      (None, None, None, 1 0           batch_normalization_2[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "resize_image_3 (resize_image)   (None, None, None, 1 0           activation_51[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_3 (Concatenate)     (None, None, None, 3 0           resize_image_3[0][0]             \n",
      "                                                                 activation_7[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_6 (Conv2D)               (None, None, None, 1 442496      concatenate_3[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_3 (BatchNor (None, None, None, 1 512         conv2d_6[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "activation_52 (Activation)      (None, None, None, 1 0           batch_normalization_3[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "resize_image_4 (resize_image)   (None, None, None, 1 0           activation_52[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_4 (Concatenate)     (None, None, None, 1 0           resize_image_4[0][0]             \n",
      "                                                                 activation_1[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_7 (Conv2D)               (None, None, None, 1 221312      concatenate_4[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_4 (BatchNor (None, None, None, 1 512         conv2d_7[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "resize_image_5 (resize_image)   (None, None, None, 2 0           conv2d_1[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "resize_image_6 (resize_image)   (None, None, None, 1 0           activation_50[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "resize_image_7 (resize_image)   (None, None, None, 1 0           activation_51[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "resize_image_8 (resize_image)   (None, None, None, 1 0           activation_52[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_53 (Activation)      (None, None, None, 1 0           batch_normalization_4[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_5 (Concatenate)     (None, None, None, 7 0           resize_image_5[0][0]             \n",
      "                                                                 resize_image_6[0][0]             \n",
      "                                                                 resize_image_7[0][0]             \n",
      "                                                                 resize_image_8[0][0]             \n",
      "                                                                 activation_53[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_8 (Conv2D)               (None, None, None, 2 1769728     concatenate_5[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_5 (BatchNor (None, None, None, 2 1024        conv2d_8[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "activation_54 (Activation)      (None, None, None, 2 0           batch_normalization_5[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_9 (Conv2D)               (None, None, None, 6 1542        activation_54[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "resize_image_9 (resize_image)   (None, None, None, 6 0           conv2d_9[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "activation_55 (Activation)      (None, None, None, 6 0           resize_image_9[0][0]             \n",
      "==================================================================================================\n",
      "Total params: 27,976,582\n",
      "Trainable params: 27,921,926\n",
      "Non-trainable params: 54,656\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "inputs = keras.layers.Input(shape=shape)\n",
    "output = psenet(inputs)\n",
    "model  = keras.models.Model(inputs,output)\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from keras.optimizers import Adam\n",
    "from models.loss import build_loss\n",
    "from models.metrics import build_iou,mean_iou\n",
    "from keras.utils import multi_gpu_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(None, None)\n",
      "(None, None)\n",
      "(None, None)\n",
      "(None, None)\n",
      "(None, None)\n",
      "(None, None)\n",
      "(None, None)\n",
      "(None, None)\n",
      "(None, None)\n",
      "(None, None)\n",
      "(None, None)\n",
      "(None, None)\n",
      "(None, None)\n",
      "(None, None)\n",
      "(None, None)\n",
      "(None, None)\n",
      "(None, None)\n",
      "(None, None)\n"
     ]
    }
   ],
   "source": [
    "parallel_model = multi_gpu_model(model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "adam = Adam(1e-3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "ious = build_iou([0,1],['bk','txt'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "parallel_model.compile(loss=build_loss,\n",
    "              optimizer=adam,\n",
    "              metrics=ious)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import config \n",
    "from tool.generator import Generator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "train_dir = config.MIWI_2018_TRAIN_LABEL_DIR\n",
    "test_dir = config.MIWI_2018_TEST_LABEL_DIR\n",
    "batch_size = 4\n",
    "num_class =2 \n",
    "shape = (640,640)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "gen_train = Generator(train_dir,batch_size = batch_size ,istraining=True,num_classes=num_class,mirror = False,reshape=shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "gen_test = Generator(test_dir,batch_size = batch_size ,istraining=False,num_classes=num_class,\n",
    "                     reshape=shape,mirror=False,scale=False,clip=False,trans_color=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from keras.callbacks import ModelCheckpoint\n",
    "from keras.callbacks import TensorBoard\n",
    "checkpoint = ModelCheckpoint(r'resent50-190126-{epoch:02d}.hdf5',\n",
    "                           save_weights_only=True)\n",
    "tb = TensorBoard(log_dir='./logs', update_freq=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "2227/2227 [==============================] - 1939s 871ms/step - loss: 0.2079 - iou_bk: 0.9226 - iou_txt: 0.6699 - val_loss: 0.1706 - val_iou_bk: 0.9339 - val_iou_txt: 0.7301\n",
      "Epoch 2/100\n",
      "2227/2227 [==============================] - 1943s 873ms/step - loss: 0.1867 - iou_bk: 0.9309 - iou_txt: 0.7004 - val_loss: 0.1572 - val_iou_bk: 0.9409 - val_iou_txt: 0.7490\n",
      "Epoch 3/100\n",
      "2227/2227 [==============================] - 1942s 872ms/step - loss: 0.1773 - iou_bk: 0.9352 - iou_txt: 0.7146 - val_loss: 0.1484 - val_iou_bk: 0.9458 - val_iou_txt: 0.7588\n",
      "Epoch 4/100\n",
      "2227/2227 [==============================] - 1940s 871ms/step - loss: 0.1686 - iou_bk: 0.9378 - iou_txt: 0.7277 - val_loss: 0.1491 - val_iou_bk: 0.9420 - val_iou_txt: 0.7619\n",
      "Epoch 5/100\n",
      "2227/2227 [==============================] - 1940s 871ms/step - loss: 0.1617 - iou_bk: 0.9410 - iou_txt: 0.7385 - val_loss: 0.1512 - val_iou_bk: 0.9418 - val_iou_txt: 0.7577\n",
      "Epoch 6/100\n",
      "2227/2227 [==============================] - 1958s 879ms/step - loss: 0.1598 - iou_bk: 0.9415 - iou_txt: 0.7411 - val_loss: 0.1498 - val_iou_bk: 0.9416 - val_iou_txt: 0.7609\n",
      "Epoch 7/100\n",
      "2227/2227 [==============================] - 1942s 872ms/step - loss: 0.1528 - iou_bk: 0.9443 - iou_txt: 0.7521 - val_loss: 0.1352 - val_iou_bk: 0.9510 - val_iou_txt: 0.7805\n",
      "Epoch 8/100\n",
      "2227/2227 [==============================] - 1944s 873ms/step - loss: 0.1506 - iou_bk: 0.9453 - iou_txt: 0.7560 - val_loss: 0.1355 - val_iou_bk: 0.9492 - val_iou_txt: 0.7692\n",
      "Epoch 9/100\n",
      "2227/2227 [==============================] - 1950s 875ms/step - loss: 0.1478 - iou_bk: 0.9460 - iou_txt: 0.7594 - val_loss: 0.1345 - val_iou_bk: 0.9497 - val_iou_txt: 0.7838\n",
      "Epoch 10/100\n",
      "2227/2227 [==============================] - 1952s 876ms/step - loss: 0.1449 - iou_bk: 0.9477 - iou_txt: 0.7648 - val_loss: 0.1282 - val_iou_bk: 0.9520 - val_iou_txt: 0.7903\n",
      "Epoch 11/100\n",
      "2227/2227 [==============================] - 1948s 875ms/step - loss: 0.1439 - iou_bk: 0.9477 - iou_txt: 0.7658 - val_loss: 0.1261 - val_iou_bk: 0.9534 - val_iou_txt: 0.7913\n",
      "Epoch 12/100\n",
      "2227/2227 [==============================] - 1949s 875ms/step - loss: 0.1402 - iou_bk: 0.9492 - iou_txt: 0.7721 - val_loss: 0.1234 - val_iou_bk: 0.9538 - val_iou_txt: 0.8005\n",
      "Epoch 13/100\n",
      "2227/2227 [==============================] - 1953s 877ms/step - loss: 0.1405 - iou_bk: 0.9493 - iou_txt: 0.7706 - val_loss: 0.1240 - val_iou_bk: 0.9532 - val_iou_txt: 0.8007\n",
      "Epoch 14/100\n",
      "2227/2227 [==============================] - 1940s 871ms/step - loss: 0.1351 - iou_bk: 0.9508 - iou_txt: 0.7790 - val_loss: 0.1217 - val_iou_bk: 0.9538 - val_iou_txt: 0.8019\n",
      "Epoch 15/100\n",
      "2227/2227 [==============================] - 1946s 874ms/step - loss: 0.1349 - iou_bk: 0.9509 - iou_txt: 0.7790 - val_loss: 0.1235 - val_iou_bk: 0.9536 - val_iou_txt: 0.8002\n",
      "Epoch 16/100\n",
      "2227/2227 [==============================] - 1941s 872ms/step - loss: 0.1351 - iou_bk: 0.9510 - iou_txt: 0.7795 - val_loss: 0.1168 - val_iou_bk: 0.9571 - val_iou_txt: 0.8114\n",
      "Epoch 17/100\n",
      "2227/2227 [==============================] - 1940s 871ms/step - loss: 0.1326 - iou_bk: 0.9514 - iou_txt: 0.7835 - val_loss: 0.1147 - val_iou_bk: 0.9577 - val_iou_txt: 0.8099\n",
      "Epoch 18/100\n",
      "2227/2227 [==============================] - 1942s 872ms/step - loss: 0.1300 - iou_bk: 0.9522 - iou_txt: 0.7872 - val_loss: 0.1174 - val_iou_bk: 0.9557 - val_iou_txt: 0.8074\n",
      "Epoch 19/100\n",
      "2227/2227 [==============================] - 1944s 873ms/step - loss: 0.1299 - iou_bk: 0.9527 - iou_txt: 0.7872 - val_loss: 0.1471 - val_iou_bk: 0.9433 - val_iou_txt: 0.7696\n",
      "Epoch 20/100\n",
      "2227/2227 [==============================] - 1953s 877ms/step - loss: 0.1309 - iou_bk: 0.9526 - iou_txt: 0.7858 - val_loss: 0.1174 - val_iou_bk: 0.9560 - val_iou_txt: 0.8090\n",
      "Epoch 21/100\n",
      "2227/2227 [==============================] - 1952s 876ms/step - loss: 0.1281 - iou_bk: 0.9536 - iou_txt: 0.7901 - val_loss: 0.1154 - val_iou_bk: 0.9565 - val_iou_txt: 0.8070\n",
      "Epoch 22/100\n",
      "2227/2227 [==============================] - 1948s 875ms/step - loss: 0.1277 - iou_bk: 0.9534 - iou_txt: 0.7906 - val_loss: 0.1182 - val_iou_bk: 0.9557 - val_iou_txt: 0.8055\n",
      "Epoch 23/100\n",
      "2227/2227 [==============================] - 1952s 877ms/step - loss: 0.1292 - iou_bk: 0.9532 - iou_txt: 0.7882 - val_loss: 0.1096 - val_iou_bk: 0.9594 - val_iou_txt: 0.8167\n",
      "Epoch 24/100\n",
      "2227/2227 [==============================] - 1950s 875ms/step - loss: 0.1261 - iou_bk: 0.9545 - iou_txt: 0.7933 - val_loss: 0.1143 - val_iou_bk: 0.9580 - val_iou_txt: 0.8164\n",
      "Epoch 25/100\n",
      "2227/2227 [==============================] - 1954s 877ms/step - loss: 0.1252 - iou_bk: 0.9549 - iou_txt: 0.7949 - val_loss: 0.1155 - val_iou_bk: 0.9565 - val_iou_txt: 0.8109\n",
      "Epoch 26/100\n",
      "2227/2227 [==============================] - 1948s 875ms/step - loss: 0.1225 - iou_bk: 0.9557 - iou_txt: 0.7991 - val_loss: 0.1145 - val_iou_bk: 0.9581 - val_iou_txt: 0.8159\n",
      "Epoch 27/100\n",
      "2227/2227 [==============================] - 1944s 873ms/step - loss: 0.1240 - iou_bk: 0.9549 - iou_txt: 0.7962 - val_loss: 0.1113 - val_iou_bk: 0.9585 - val_iou_txt: 0.8141\n",
      "Epoch 28/100\n",
      "2227/2227 [==============================] - 1944s 873ms/step - loss: 0.1219 - iou_bk: 0.9557 - iou_txt: 0.7999 - val_loss: 0.1372 - val_iou_bk: 0.9454 - val_iou_txt: 0.7817\n",
      "Epoch 29/100\n",
      "2227/2227 [==============================] - 1948s 875ms/step - loss: 0.1240 - iou_bk: 0.9549 - iou_txt: 0.7962 - val_loss: 0.1114 - val_iou_bk: 0.9592 - val_iou_txt: 0.8205\n",
      "Epoch 30/100\n",
      "2227/2227 [==============================] - 1948s 875ms/step - loss: 0.1214 - iou_bk: 0.9562 - iou_txt: 0.8014 - val_loss: 0.1109 - val_iou_bk: 0.9579 - val_iou_txt: 0.8177\n",
      "Epoch 31/100\n",
      "2227/2227 [==============================] - 1947s 874ms/step - loss: 0.1225 - iou_bk: 0.9552 - iou_txt: 0.7993 - val_loss: 0.1106 - val_iou_bk: 0.9591 - val_iou_txt: 0.8207\n",
      "Epoch 32/100\n",
      "2227/2227 [==============================] - 1950s 875ms/step - loss: 0.1198 - iou_bk: 0.9569 - iou_txt: 0.8038 - val_loss: 0.1109 - val_iou_bk: 0.9584 - val_iou_txt: 0.8189\n",
      "Epoch 33/100\n",
      "2227/2227 [==============================] - 1949s 875ms/step - loss: 0.1199 - iou_bk: 0.9565 - iou_txt: 0.8037 - val_loss: 0.1094 - val_iou_bk: 0.9599 - val_iou_txt: 0.8176\n",
      "Epoch 34/100\n",
      "2227/2227 [==============================] - 1948s 875ms/step - loss: 0.1185 - iou_bk: 0.9573 - iou_txt: 0.8058 - val_loss: 0.1121 - val_iou_bk: 0.9578 - val_iou_txt: 0.8156\n",
      "Epoch 35/100\n",
      "2227/2227 [==============================] - 1947s 874ms/step - loss: 0.1172 - iou_bk: 0.9573 - iou_txt: 0.8079 - val_loss: 0.1114 - val_iou_bk: 0.9582 - val_iou_txt: 0.8154\n",
      "Epoch 36/100\n",
      "2227/2227 [==============================] - 1952s 876ms/step - loss: 0.1174 - iou_bk: 0.9580 - iou_txt: 0.8074 - val_loss: 0.1097 - val_iou_bk: 0.9594 - val_iou_txt: 0.8179\n",
      "Epoch 37/100\n",
      "2227/2227 [==============================] - 1948s 875ms/step - loss: 0.1164 - iou_bk: 0.9577 - iou_txt: 0.8088 - val_loss: 0.1112 - val_iou_bk: 0.9575 - val_iou_txt: 0.8165\n",
      "Epoch 38/100\n",
      "2227/2227 [==============================] - 1947s 874ms/step - loss: 0.1154 - iou_bk: 0.9585 - iou_txt: 0.8105 - val_loss: 0.1048 - val_iou_bk: 0.9613 - val_iou_txt: 0.8280\n",
      "Epoch 39/100\n",
      "2227/2227 [==============================] - 1946s 874ms/step - loss: 0.1162 - iou_bk: 0.9584 - iou_txt: 0.8090 - val_loss: 0.1050 - val_iou_bk: 0.9603 - val_iou_txt: 0.8238\n",
      "Epoch 40/100\n",
      "2227/2227 [==============================] - 1947s 874ms/step - loss: 0.1160 - iou_bk: 0.9578 - iou_txt: 0.8094 - val_loss: 0.1096 - val_iou_bk: 0.9590 - val_iou_txt: 0.8217\n",
      "Epoch 41/100\n",
      "2227/2227 [==============================] - 1956s 878ms/step - loss: 0.1137 - iou_bk: 0.9587 - iou_txt: 0.8130 - val_loss: 0.1046 - val_iou_bk: 0.9610 - val_iou_txt: 0.8265\n",
      "Epoch 42/100\n",
      "2227/2227 [==============================] - 1943s 872ms/step - loss: 0.1148 - iou_bk: 0.9585 - iou_txt: 0.8117 - val_loss: 0.1043 - val_iou_bk: 0.9613 - val_iou_txt: 0.8267\n",
      "Epoch 43/100\n",
      "2227/2227 [==============================] - 1942s 872ms/step - loss: 0.1133 - iou_bk: 0.9591 - iou_txt: 0.8144 - val_loss: 0.1073 - val_iou_bk: 0.9594 - val_iou_txt: 0.8231\n",
      "Epoch 44/100\n",
      "2227/2227 [==============================] - 1955s 878ms/step - loss: 0.1139 - iou_bk: 0.9587 - iou_txt: 0.8128 - val_loss: 0.1047 - val_iou_bk: 0.9605 - val_iou_txt: 0.8259\n",
      "Epoch 45/100\n",
      "2227/2227 [==============================] - 1946s 874ms/step - loss: 0.1153 - iou_bk: 0.9587 - iou_txt: 0.8107 - val_loss: 0.1041 - val_iou_bk: 0.9613 - val_iou_txt: 0.8276\n",
      "Epoch 46/100\n",
      "2227/2227 [==============================] - 1945s 873ms/step - loss: 0.1137 - iou_bk: 0.9590 - iou_txt: 0.8132 - val_loss: 0.1065 - val_iou_bk: 0.9595 - val_iou_txt: 0.8255\n",
      "Epoch 47/100\n",
      "2227/2227 [==============================] - 1954s 877ms/step - loss: 0.1127 - iou_bk: 0.9596 - iou_txt: 0.8146 - val_loss: 0.1052 - val_iou_bk: 0.9602 - val_iou_txt: 0.8267\n",
      "Epoch 48/100\n",
      "2227/2227 [==============================] - 1946s 874ms/step - loss: 0.1115 - iou_bk: 0.9598 - iou_txt: 0.8169 - val_loss: 0.1075 - val_iou_bk: 0.9602 - val_iou_txt: 0.8265\n",
      "Epoch 49/100\n",
      "2227/2227 [==============================] - 1951s 876ms/step - loss: 0.1133 - iou_bk: 0.9593 - iou_txt: 0.8139 - val_loss: 0.1016 - val_iou_bk: 0.9620 - val_iou_txt: 0.8312\n",
      "Epoch 50/100\n",
      "2227/2227 [==============================] - 1941s 872ms/step - loss: 0.1115 - iou_bk: 0.9600 - iou_txt: 0.8170 - val_loss: 0.1058 - val_iou_bk: 0.9605 - val_iou_txt: 0.8222\n",
      "Epoch 51/100\n",
      "2227/2227 [==============================] - 1950s 876ms/step - loss: 0.1129 - iou_bk: 0.9594 - iou_txt: 0.8141 - val_loss: 0.1024 - val_iou_bk: 0.9622 - val_iou_txt: 0.8317\n",
      "Epoch 52/100\n",
      "2227/2227 [==============================] - 1942s 872ms/step - loss: 0.1097 - iou_bk: 0.9603 - iou_txt: 0.8199 - val_loss: 0.1048 - val_iou_bk: 0.9610 - val_iou_txt: 0.8228\n",
      "Epoch 53/100\n",
      "2227/2227 [==============================] - 1950s 875ms/step - loss: 0.1111 - iou_bk: 0.9599 - iou_txt: 0.8174 - val_loss: 0.1029 - val_iou_bk: 0.9614 - val_iou_txt: 0.8284\n",
      "Epoch 54/100\n",
      "2227/2227 [==============================] - 1952s 876ms/step - loss: 0.1094 - iou_bk: 0.9605 - iou_txt: 0.8203 - val_loss: 0.1018 - val_iou_bk: 0.9619 - val_iou_txt: 0.8310\n",
      "Epoch 55/100\n",
      "2227/2227 [==============================] - 1947s 874ms/step - loss: 0.1086 - iou_bk: 0.9611 - iou_txt: 0.8214 - val_loss: 0.1051 - val_iou_bk: 0.9602 - val_iou_txt: 0.8274\n",
      "Epoch 56/100\n",
      "2227/2227 [==============================] - 1953s 877ms/step - loss: 0.1085 - iou_bk: 0.9612 - iou_txt: 0.8220 - val_loss: 0.1022 - val_iou_bk: 0.9612 - val_iou_txt: 0.8299\n",
      "Epoch 57/100\n",
      "2227/2227 [==============================] - 1946s 874ms/step - loss: 0.1083 - iou_bk: 0.9607 - iou_txt: 0.8222 - val_loss: 0.1028 - val_iou_bk: 0.9617 - val_iou_txt: 0.8277\n",
      "Epoch 58/100\n",
      "2227/2227 [==============================] - 1947s 874ms/step - loss: 0.1099 - iou_bk: 0.9602 - iou_txt: 0.8193 - val_loss: 0.1011 - val_iou_bk: 0.9624 - val_iou_txt: 0.8328\n",
      "Epoch 59/100\n",
      "2227/2227 [==============================] - 1954s 877ms/step - loss: 0.1075 - iou_bk: 0.9614 - iou_txt: 0.8229 - val_loss: 0.1057 - val_iou_bk: 0.9596 - val_iou_txt: 0.8239\n",
      "Epoch 60/100\n",
      "2227/2227 [==============================] - 1951s 876ms/step - loss: 0.1098 - iou_bk: 0.9605 - iou_txt: 0.8198 - val_loss: 0.0994 - val_iou_bk: 0.9629 - val_iou_txt: 0.8355\n",
      "Epoch 61/100\n",
      "2227/2227 [==============================] - 1949s 875ms/step - loss: 0.1074 - iou_bk: 0.9616 - iou_txt: 0.8237 - val_loss: 0.1047 - val_iou_bk: 0.9608 - val_iou_txt: 0.8291\n",
      "Epoch 62/100\n",
      "2227/2227 [==============================] - 1941s 872ms/step - loss: 0.1073 - iou_bk: 0.9614 - iou_txt: 0.8244 - val_loss: 0.1012 - val_iou_bk: 0.9624 - val_iou_txt: 0.8341\n",
      "Epoch 63/100\n",
      "2227/2227 [==============================] - 1948s 875ms/step - loss: 0.1080 - iou_bk: 0.9609 - iou_txt: 0.8218 - val_loss: 0.1048 - val_iou_bk: 0.9614 - val_iou_txt: 0.8252\n",
      "Epoch 64/100\n",
      "2227/2227 [==============================] - 1949s 875ms/step - loss: 0.1069 - iou_bk: 0.9616 - iou_txt: 0.8248 - val_loss: 0.0989 - val_iou_bk: 0.9631 - val_iou_txt: 0.8332\n",
      "Epoch 65/100\n",
      "2227/2227 [==============================] - 1950s 876ms/step - loss: 0.1067 - iou_bk: 0.9620 - iou_txt: 0.8247 - val_loss: 0.1109 - val_iou_bk: 0.9592 - val_iou_txt: 0.8252\n",
      "Epoch 66/100\n",
      "2227/2227 [==============================] - 1940s 871ms/step - loss: 0.1066 - iou_bk: 0.9617 - iou_txt: 0.8252 - val_loss: 0.1010 - val_iou_bk: 0.9623 - val_iou_txt: 0.8332\n",
      "Epoch 67/100\n",
      "2227/2227 [==============================] - 1947s 874ms/step - loss: 0.1062 - iou_bk: 0.9619 - iou_txt: 0.8261 - val_loss: 0.1036 - val_iou_bk: 0.9614 - val_iou_txt: 0.8290\n",
      "Epoch 68/100\n",
      "2227/2227 [==============================] - 1949s 875ms/step - loss: 0.1055 - iou_bk: 0.9622 - iou_txt: 0.8267 - val_loss: 0.0975 - val_iou_bk: 0.9638 - val_iou_txt: 0.8362\n",
      "Epoch 69/100\n",
      "2227/2227 [==============================] - 1944s 873ms/step - loss: 0.1043 - iou_bk: 0.9626 - iou_txt: 0.8290 - val_loss: 0.1000 - val_iou_bk: 0.9627 - val_iou_txt: 0.8301\n",
      "Epoch 70/100\n",
      "2227/2227 [==============================] - 1949s 875ms/step - loss: 0.1055 - iou_bk: 0.9627 - iou_txt: 0.8268 - val_loss: 0.1025 - val_iou_bk: 0.9615 - val_iou_txt: 0.8328\n",
      "Epoch 71/100\n",
      "1009/2227 [============>.................] - ETA: 16:25 - loss: 0.1042 - iou_bk: 0.9628 - iou_txt: 0.8289"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-21-2a95528604bc>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      8\u001b[0m                           \u001b[0mworkers\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m4\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m      9\u001b[0m                           \u001b[0mmax_queue_size\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m16\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m---> 10\u001b[0;31m                           callbacks=[checkpoint,tb])\n\u001b[0m",
      "\u001b[0;32mC:\\Program Files\\Anaconda3\\lib\\site-packages\\keras\\legacy\\interfaces.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     89\u001b[0m                 warnings.warn('Update your `' + object_name + '` call to the ' +\n\u001b[1;32m     90\u001b[0m                               'Keras 2 API: ' + signature, stacklevel=2)\n\u001b[0;32m---> 91\u001b[0;31m             \u001b[1;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     92\u001b[0m         \u001b[0mwrapper\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_original_function\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m     93\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mC:\\Program Files\\Anaconda3\\lib\\site-packages\\keras\\engine\\training.py\u001b[0m in \u001b[0;36mfit_generator\u001b[0;34m(self, generator, steps_per_epoch, epochs, verbose, callbacks, validation_data, validation_steps, class_weight, max_queue_size, workers, use_multiprocessing, shuffle, initial_epoch)\u001b[0m\n\u001b[1;32m   1416\u001b[0m             \u001b[0muse_multiprocessing\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0muse_multiprocessing\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m   1417\u001b[0m             \u001b[0mshuffle\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mshuffle\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1418\u001b[0;31m             initial_epoch=initial_epoch)\n\u001b[0m\u001b[1;32m   1419\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m   1420\u001b[0m     \u001b[1;33m@\u001b[0m\u001b[0minterfaces\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlegacy_generator_methods_support\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mC:\\Program Files\\Anaconda3\\lib\\site-packages\\keras\\engine\\training_generator.py\u001b[0m in \u001b[0;36mfit_generator\u001b[0;34m(model, generator, steps_per_epoch, epochs, verbose, callbacks, validation_data, validation_steps, class_weight, max_queue_size, workers, use_multiprocessing, shuffle, initial_epoch)\u001b[0m\n\u001b[1;32m    215\u001b[0m                 outs = model.train_on_batch(x, y,\n\u001b[1;32m    216\u001b[0m                                             \u001b[0msample_weight\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0msample_weight\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m--> 217\u001b[0;31m                                             class_weight=class_weight)\n\u001b[0m\u001b[1;32m    218\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m    219\u001b[0m                 \u001b[0mouts\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mto_list\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mouts\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mC:\\Program Files\\Anaconda3\\lib\\site-packages\\keras\\engine\\training.py\u001b[0m in \u001b[0;36mtrain_on_batch\u001b[0;34m(self, x, y, sample_weight, class_weight)\u001b[0m\n\u001b[1;32m   1215\u001b[0m             \u001b[0mins\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mx\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0my\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0msample_weights\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m   1216\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_make_train_function\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1217\u001b[0;31m         \u001b[0moutputs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtrain_function\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mins\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1218\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0munpack_singleton\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0moutputs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m   1219\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mC:\\Program Files\\Anaconda3\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, inputs)\u001b[0m\n\u001b[1;32m   2713\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_legacy_call\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m   2714\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2715\u001b[0;31m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_call\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2716\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m   2717\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mpy_any\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mis_tensor\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[1;32min\u001b[0m \u001b[0minputs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mC:\\Program Files\\Anaconda3\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py\u001b[0m in \u001b[0;36m_call\u001b[0;34m(self, inputs)\u001b[0m\n\u001b[1;32m   2673\u001b[0m             \u001b[0mfetched\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_callable_fn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0marray_vals\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrun_metadata\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrun_metadata\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m   2674\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2675\u001b[0;31m             \u001b[0mfetched\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_callable_fn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0marray_vals\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2676\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mfetched\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0moutputs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m   2677\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mC:\\Program Files\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\client\\session.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1397\u001b[0m           ret = tf_session.TF_SessionRunCallable(\n\u001b[1;32m   1398\u001b[0m               \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_session\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_session\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_handle\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mstatus\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1399\u001b[0;31m               run_metadata_ptr)\n\u001b[0m\u001b[1;32m   1400\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mrun_metadata\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m   1401\u001b[0m           \u001b[0mproto_data\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtf_session\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mTF_GetBuffer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrun_metadata_ptr\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "res = parallel_model.fit_generator(gen_train,\n",
    "                          steps_per_epoch =gen_train.num_samples()// batch_size,\n",
    "                          epochs = 100,\n",
    "                          validation_data=gen_test,\n",
    "                          validation_steps =gen_test.num_samples()//batch_size,\n",
    "                          verbose=1,\n",
    "                          initial_epoch=0,\n",
    "                          workers=4,\n",
    "                          max_queue_size=16,\n",
    "                          callbacks=[checkpoint,tb])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 71/100\n",
      "2227/2227 [==============================] - 1952s 876ms/step - loss: 0.1040 - iou_bk: 0.9624 - iou_txt: 0.8288 - val_loss: 0.0968 - val_iou_bk: 0.9640 - val_iou_txt: 0.8388\n",
      "Epoch 72/100\n",
      "2227/2227 [==============================] - 1934s 868ms/step - loss: 0.1037 - iou_bk: 0.9627 - iou_txt: 0.8289 - val_loss: 0.0993 - val_iou_bk: 0.9629 - val_iou_txt: 0.8316\n",
      "Epoch 73/100\n",
      "1623/2227 [====================>.........] - ETA: 8:10 - loss: 0.1055 - iou_bk: 0.9618 - iou_txt: 0.8266"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-22-4a40d5f696cc>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      9\u001b[0m                           \u001b[0mworkers\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m4\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m     10\u001b[0m                           \u001b[0mmax_queue_size\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m16\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m---> 11\u001b[0;31m                           callbacks=[checkpoint,tb])\n\u001b[0m",
      "\u001b[0;32mC:\\Program Files\\Anaconda3\\lib\\site-packages\\keras\\legacy\\interfaces.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     89\u001b[0m                 warnings.warn('Update your `' + object_name + '` call to the ' +\n\u001b[1;32m     90\u001b[0m                               'Keras 2 API: ' + signature, stacklevel=2)\n\u001b[0;32m---> 91\u001b[0;31m             \u001b[1;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     92\u001b[0m         \u001b[0mwrapper\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_original_function\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m     93\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mC:\\Program Files\\Anaconda3\\lib\\site-packages\\keras\\engine\\training.py\u001b[0m in \u001b[0;36mfit_generator\u001b[0;34m(self, generator, steps_per_epoch, epochs, verbose, callbacks, validation_data, validation_steps, class_weight, max_queue_size, workers, use_multiprocessing, shuffle, initial_epoch)\u001b[0m\n\u001b[1;32m   1416\u001b[0m             \u001b[0muse_multiprocessing\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0muse_multiprocessing\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m   1417\u001b[0m             \u001b[0mshuffle\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mshuffle\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1418\u001b[0;31m             initial_epoch=initial_epoch)\n\u001b[0m\u001b[1;32m   1419\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m   1420\u001b[0m     \u001b[1;33m@\u001b[0m\u001b[0minterfaces\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlegacy_generator_methods_support\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mC:\\Program Files\\Anaconda3\\lib\\site-packages\\keras\\engine\\training_generator.py\u001b[0m in \u001b[0;36mfit_generator\u001b[0;34m(model, generator, steps_per_epoch, epochs, verbose, callbacks, validation_data, validation_steps, class_weight, max_queue_size, workers, use_multiprocessing, shuffle, initial_epoch)\u001b[0m\n\u001b[1;32m    215\u001b[0m                 outs = model.train_on_batch(x, y,\n\u001b[1;32m    216\u001b[0m                                             \u001b[0msample_weight\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0msample_weight\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m--> 217\u001b[0;31m                                             class_weight=class_weight)\n\u001b[0m\u001b[1;32m    218\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m    219\u001b[0m                 \u001b[0mouts\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mto_list\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mouts\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mC:\\Program Files\\Anaconda3\\lib\\site-packages\\keras\\engine\\training.py\u001b[0m in \u001b[0;36mtrain_on_batch\u001b[0;34m(self, x, y, sample_weight, class_weight)\u001b[0m\n\u001b[1;32m   1215\u001b[0m             \u001b[0mins\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mx\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0my\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0msample_weights\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m   1216\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_make_train_function\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1217\u001b[0;31m         \u001b[0moutputs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtrain_function\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mins\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1218\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0munpack_singleton\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0moutputs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m   1219\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mC:\\Program Files\\Anaconda3\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, inputs)\u001b[0m\n\u001b[1;32m   2713\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_legacy_call\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m   2714\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2715\u001b[0;31m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_call\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2716\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m   2717\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mpy_any\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mis_tensor\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[1;32min\u001b[0m \u001b[0minputs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mC:\\Program Files\\Anaconda3\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py\u001b[0m in \u001b[0;36m_call\u001b[0;34m(self, inputs)\u001b[0m\n\u001b[1;32m   2673\u001b[0m             \u001b[0mfetched\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_callable_fn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0marray_vals\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrun_metadata\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrun_metadata\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m   2674\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2675\u001b[0;31m             \u001b[0mfetched\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_callable_fn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0marray_vals\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2676\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mfetched\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0moutputs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m   2677\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mC:\\Program Files\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\client\\session.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1397\u001b[0m           ret = tf_session.TF_SessionRunCallable(\n\u001b[1;32m   1398\u001b[0m               \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_session\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_session\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_handle\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mstatus\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1399\u001b[0;31m               run_metadata_ptr)\n\u001b[0m\u001b[1;32m   1400\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mrun_metadata\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m   1401\u001b[0m           \u001b[0mproto_data\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtf_session\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mTF_GetBuffer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrun_metadata_ptr\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "parallel_model.optimizer.lr = 1e-4\n",
    "res = parallel_model.fit_generator(gen_train,\n",
    "                          steps_per_epoch =gen_train.num_samples()// batch_size,\n",
    "                          epochs = 100,\n",
    "                          validation_data=gen_test,\n",
    "                          validation_steps =gen_test.num_samples()//batch_size,\n",
    "                          verbose=1,\n",
    "                          initial_epoch=70,\n",
    "                          workers=4,\n",
    "                          max_queue_size=16,\n",
    "                          callbacks=[checkpoint,tb])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#model.load_weights('resent50-29.hdf5')\n",
    "parallel_model.load_weights('resent50-190126-71.hdf5')\n",
    "model.save_weights('resnet50-190126-iou8388.hdf5')\n",
    "model.load_weights('resnet50-190126-iou8388.hdf5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'gen_test' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-7-b6ab4c190477>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mimages\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mlables\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnext\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mgen_test\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'gen_test' is not defined"
     ]
    }
   ],
   "source": [
    "images,lables = next(gen_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import glob\n",
    "import cv2\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np \n",
    "import os\n",
    "import tqdm\n",
    "#dir = r'C:\\jianweidata\\ocr\\psenet\\Extraction Test\\2x'\n",
    "dir = r'E:\\psenet-MTWI\\document\\mtwi_2018_task2_test\\icpr_mtwi_task2\\image_test'\n",
    "imagesfile = glob.glob(os.path.join(dir,'*.jpg'))\n",
    "MIN_LEN = 640\n",
    "MAX_LEN = 1024"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|████████████████████████████████████████████████████████████████████████████| 10000/10000 [36:21<00:00,  4.78it/s]\n"
     ]
    }
   ],
   "source": [
    "from tool.utils import ufunc_4 , scale_expand_kernels ,fit_minarearectange,fit_boundingRect,save_MTWI_2108_resault\n",
    "with tqdm.tqdm(total = len(imagesfile)) as bar:\n",
    "    for i,j in enumerate(imagesfile):\n",
    "                bar.update()\n",
    "                #j = r'E:\\psenet-MTWI\\document\\mtwi_2018_task2_test\\icpr_mtwi_task2\\image_test\\T1..67FbxfXXXXXXXX_!!0-item_pic.jpg.jpg'\n",
    "                images=cv2.imdecode(np.fromfile(j,dtype=np.uint8),-1) \n",
    "                #images=cv2.cvtColor(images,cv2.COLOR_RGB2BGR)\n",
    "                \n",
    "                h,w = images.shape[0:2]\n",
    "#                 if(h>w and h>MAX_LEN):\n",
    "#                     w = MAX_LEN / h * w   \n",
    "#                     h = MAX_LEN\n",
    "#                 elif(w>=h and w>MAX_LEN):\n",
    "#                     h = MAX_LEN / w * h   \n",
    "#                     w = MAX_LEN\n",
    "\n",
    "                if(w<h and w<MIN_LEN):\n",
    "                    h = MIN_LEN / w * h               \n",
    "                    w = MIN_LEN\n",
    "                elif(h<=w and h<MIN_LEN):\n",
    "                    w = MIN_LEN / h * w         \n",
    "                    h = MIN_LEN\n",
    "                    \n",
    "                w = min(w,MAX_LEN)    \n",
    "                h = min(h,MAX_LEN)\n",
    "                \n",
    "                \n",
    "                w = int(w //32 * 32)\n",
    "                h = int(h//32 * 32)\n",
    "                \n",
    "#                 w = 640\n",
    "#                 h = 640 \n",
    "                scalex = images.shape[1] / w\n",
    "                scaley = images.shape[0] / h\n",
    "                \n",
    "                images = cv2.resize(images,(w,h),cv2.INTER_AREA)\n",
    "                images = np.reshape(images,(1,h,w,3))            \n",
    "                \n",
    "                res = model.predict(images)\n",
    "                res1 = res[0]\n",
    "                res1[res1>0.9]= 1\n",
    "                res1[res1<=0.9]= 0\n",
    "                newres1 = []\n",
    "                for i in range(5):\n",
    "                    n = np.logical_and(res1[:,:,5],res1[:,:,i]) * 255\n",
    "                    newres1.append(n)\n",
    "                newres1.append(res1[:,:,5]*255)\n",
    "                num_label,labelimage = scale_expand_kernels(newres1)\n",
    "                rects = fit_minarearectange(num_label,labelimage)\n",
    "                \n",
    "                cv2.drawContours(images[0],np.array(rects)*2,-1,(0,0,255),2)\n",
    "                \n",
    "                base_name = '.'.join(os.path.basename(j).split('.')[:-1])\n",
    "                cv2.imwrite(os.path.join(dir,base_name+'_4.tif'),images[0])\n",
    "                \n",
    "                save_MTWI_2108_resault(os.path.join(dir,base_name+'.txt'),np.array(rects)*2,scalex ,scaley)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "\n",
    "MAX_LEN = 1440\n",
    "images = cv2.imread('123.jpg')\n",
    "h,w = images.shape[0:2]\n",
    "\n",
    "\n",
    "if(h>w and h>MAX_LEN):\n",
    "    w = MAX_LEN / h * w   \n",
    "    h = MAX_LEN\n",
    "elif(w>=h and w>MAX_LEN):\n",
    "    h = MAX_LEN / w * h   \n",
    "    w = MAX_LEN\n",
    "w = int(w //32 * 32)\n",
    "h = int(h//32 * 32)\n",
    "images = cv2.resize(images,(w,h),cv2.INTER_AREA)\n",
    "images = np.reshape(images,(1,h,w,3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x1c89ac754a8>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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S2Npz2sA3geZ7L9L8d+mn5gFIvYxqAUhgikQe3/zOpm9dSELzHDlcEmyS8qE3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\nZ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wThyg0IFTDEtY58wzS41oZoVvpuu1M62do3IEKAulRblEUDA238My3UyCN/aa8PLP42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\nwTVTEm1QQXotHtJYQHEKANd2rFRDEANgQ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CSosANTG7EA+kPZIhYO978jYeZg6Dawy2hB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Yqo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1bbDa5rRV7ZCVF31bZs1ysaD6tmReudnCMmCOBWnjSNOGDVeNY3G16+eAVYzidZk+MwqGmz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\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Af/wNXkd9CCJUlKBshAX+zdd0ckOKilcpH2iUy/xaYzDOEW3Rqo91Y1zWespgb5omm/6o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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c89aca0e10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(images[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 2.95 s\n"
     ]
    }
   ],
   "source": [
    "%time res = model.predict(images[0:1,:,:,:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "res1 = res[0]\n",
    "res1[res1>0.9]= 1\n",
    "res1[res1<=0.9]= 0\n",
    "newres1 = []\n",
    "for i in range(5):\n",
    "    n = np.logical_and(res1[:,:,5],res1[:,:,i]) * 255\n",
    "    newres1.append(n)\n",
    "newres1.append(res1[:,:,5]*255)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x1c89abde4a8>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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D4XA4Bjwmbul9fVuvKdir/qBVAEkOAvYF5vcVlFJWAw8AU+rU4cCotpqngGdbaiRJ0hAb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\nJEkC4P8Dwuw3/W9KSE0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c89ac7d518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(newres1[5],cmap='gray')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "_,regions = cv2.connectedComponents(newres1[0].astype('uint8'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(288, 480)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regions.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from tool.utils import ufunc_4 , scale_expand_kernels ,fit_minarearectange,fit_boundingRect"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 1.38 s\n",
      "Wall time: 11 ms\n",
      "[array([[316,  23],\n",
      "       [316,  10],\n",
      "       [397,  10],\n",
      "       [397,  23]], dtype=int64), array([[403,  23],\n",
      "       [403,  10],\n",
      "       [469,  10],\n",
      "       [469,  23]], dtype=int64), array([[ 74, 162],\n",
      "       [ 73, 150],\n",
      "       [187, 143],\n",
      "       [188, 155]], dtype=int64), array([[ 69, 278],\n",
      "       [ 11, 278],\n",
      "       [ 11, 259],\n",
      "       [ 69, 259]], dtype=int64), array([[0, 0],\n",
      "       [0, 0],\n",
      "       [0, 0],\n",
      "       [0, 0]], dtype=int64)]\n"
     ]
    },
    {
     "data": {
      "image/png": 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yFEiSJMBQIEmSKkOBJEkCDAWSJKkyFEiSJMBQIEmSqp5CQZLPJFmUZHWSoSTXJXnzNvp9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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c899792278>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%time num_label,labelimage = scale_expand_kernels(newres1)\n",
    "%time rects = fit_minarearectange(num_label,labelimage)\n",
    "plt.imshow(labelimage)\n",
    "print(rects)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labelimage[90,50]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[  0,   0, 255],\n",
       "        [  0,   0, 255],\n",
       "        [  4,   3,   0],\n",
       "        ...,\n",
       "        [  0,   1,   0],\n",
       "        [  1,   2,   0],\n",
       "        [  1,   2,   0]],\n",
       "\n",
       "       [[  0,   0, 255],\n",
       "        [ 44,  43,  39],\n",
       "        [ 46,  43,  39],\n",
       "        ...,\n",
       "        [ 31,  32,  28],\n",
       "        [ 33,  32,  28],\n",
       "        [ 31,  33,  27]],\n",
       "\n",
       "       [[ 49,  45,  40],\n",
       "        [ 48,  45,  40],\n",
       "        [ 48,  44,  39],\n",
       "        ...,\n",
       "        [ 35,  35,  29],\n",
       "        [ 37,  34,  29],\n",
       "        [ 35,  36,  27]],\n",
       "\n",
       "       ...,\n",
       "\n",
       "       [[ 42,  38,  37],\n",
       "        [ 42,  38,  37],\n",
       "        [ 41,  37,  36],\n",
       "        ...,\n",
       "        [ 81,  77,  76],\n",
       "        [ 79,  75,  74],\n",
       "        [ 78,  74,  73]],\n",
       "\n",
       "       [[ 41,  37,  36],\n",
       "        [ 41,  37,  36],\n",
       "        [ 41,  37,  36],\n",
       "        ...,\n",
       "        [ 78,  74,  73],\n",
       "        [ 75,  71,  70],\n",
       "        [ 74,  70,  69]],\n",
       "\n",
       "       [[ 41,  37,  36],\n",
       "        [ 41,  37,  36],\n",
       "        [ 40,  36,  35],\n",
       "        ...,\n",
       "        [  4,   0,   0],\n",
       "        [  5,   1,   0],\n",
       "        [  5,   1,   0]]], dtype=uint8)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "imagetest = np.copy(images[0])\n",
    "cv2.drawContours(imagetest,np.array(rects)*2,-1,(0,0,255),2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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cbr75Fm699Vbuuedei/dLjsErKg5V4cCBgxw4cJif+Zmf4c1veYs5J7ELtdxz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NBlGVq69AQZgqax77wuUeerlGzSUiBUSQ1McqDbp22cQYtXoeK4AYp5qKkgMYmtHgmi1wtOuo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4D1sr7O/QlM5uNPPhaOgULYvrGs1tIC+N3bt0xDj/Nm3lQU5JJUmo7JYvxKim355Mp4xxAZ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\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BTEbKZPEvLbsBzEIcqT4bo46TFZnIJNs8vpfS4Xp9WrbEPH9XdF2x/+qCAhH5Tz/Ca34W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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c841227860>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(imagetest)\n",
    "cv2.imwrite('test2.jpg',imagetest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "h = r'E:\\psenet-MTWI\\document\\mtwi_2018_task2_test\\icpr_mtwi_task2\\image_test\\LB1gXi2JVXXXXXUXFXXXXXXXXXX.jpg'\n",
    "images=cv2.imdecode(np.fromfile(h,dtype=np.uint8),-1) \n",
    "#images=cv2.cvtColor(images,cv2.COLOR_RGB2BGR)\n",
    "cv2.imwrite(r'E:\\tmp.jpg',images)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "## todo\n",
    "* 大概有100张图片没读出来，解决它\n",
    "* 把评测代码加上\n",
    "* 换backone"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
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